Aiming at developing a safe, intuitive, and collaborative steerable drilling robotic system for pedicle screw fixation procedures, in this paper, we leverage our recently developed steerable drilling robotic framework, and developed a collaborative drilling mode to control this system. In this control mode, first a user positions a concentric tube steerable drilling robot (CT-SDR) in the workspace and aligns it based on a preplanned trajectory. Next, the CT-SDR is directly controlled by the user through an admittance mode to perform a drilling procedure and creating a J-shape tunnel. To evaluate the user comfort and intuitiveness of the drilling procedure using this system and the proposed control interface, we performed a user study with 11 subjects, who had no prior experience in using this system. The results of this study were analyzed using various qualitative and quantitative metrics.

To address the screw loosening and pullout limitations of rigid pedicle screws in spinal fixation procedures, and to leverage our recently developed Concentric Tube Steerable Drilling Robot (CT-SDR) and Flexible Pedicle Screw (FPS), in this paper, we introduce the concept of Augmented Bridge Spinal Fixation (AB-SF). In this concept, two connecting J-shape tunnels are first drilled through pedicles of vertebra using the CT-SDR. Next, two FPSs are passed through this tunnel and bone cement is then injected through the cannulated region of the FPS to form an augmented bridge between two pedicles and reinforce strength of the fixated spine. To experimentally analyze and study the feasibility of AB-SF technique, we first used our robotic system (i.e., a CT-SDR integrated with a robotic arm) to create two different fixation scenarios in which two J-shape tunnels, forming a bridge, were drilled at different depth of a vertebral phantom. Next, we implanted two FPSs within the drilled tunnels and then successfully simulated the bone cement augmentation process.

In this paper, to collectively address the existing limitations on endoscopic diagnosis of Advanced Gastric Cancer (AGC) Tumors, for the first time, we propose (i) utilization and evaluation of our recently developed Vision-based Tactile Sensor (VTS), and (ii) a complementary Machine Learning (ML) algorithm for classifying tumors using their textural features. Leveraging a seven DoF robotic manipulator and unique custom-designed and additively-manufactured realistic AGC tumor phantoms, we demonstrated the advantages of automated data collection using the VTS addressing the problem of data scarcity and biases encountered in traditional ML-based approaches. Our synthetic-data-trained ML model was successfully evaluated and compared with traditional ML models utilizing various statistical metrics even under mixed morphological characteristics and partial sensor contact.

We present a novel Cascade Reliability Framework (CRF) that integrates two independent cascade layers of reliability (i.e., variational temperature scaling and conformal prediction) with a pre-trained Machine Learning (ML) model in order to provide clinicians with a more reliable and tunable tool for early-stage diagnosis of Colorectal Cancer (CRC) polyps. The conformal prediction layer generates predictive sets that are guaranteed to contain the true polyp type with an adjustable error rate tuned by clinicians, while the confidence calibration generates meaningful confidence estimates for each predicted label. These two layers provide additional information and an error-tuning-ability for clinicians to assist them in making informed and intuitive decisions considering the outputs of the pre-trained ML model. Utilizing a novel vision-based tactile sensor and unique 3D-printed CRC polyp phantoms, we evaluated the trustworthiness of the proposed architecture and particularly dual outputs of four different types of CRF models, integrated with two different pre-trained ML models (i.e., ResNet18 and Dilated Residual Network) to highlight the model-agnostic feature of the architecture. To thoroughly assess the performance of the proposed approach, we used reliability diagrams and metrics such as accuracy, coverage, and average set size, while also addressing inter-class performance. Results demonstrate that the calibrated CRF models are well capable of handling non-ideal inputs with noise and blur. Moreover, using the conformal prediction with a user-defined error rate and various experiments, we show how clinicians can intuitively interact with a pre-trained ML model to make informed decisions and minimize the risk of CRC polyps misdiagnoses.